Data Hiding Using LSB with QR Code Data Pattern Image

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1 IJSTE - International Journal of Science Technology & Engineering Volume 2 Issue 10 April 2016 ISSN (online): X Data Hiding Using LSB with QR Code Data Pattern Image D. Antony Praveen Kumar M. Baskaran UG Scholar UG Scholar Department of Electronics and Communication Engineering Department of Electronics and Communication Engineering J. Jocin Mr. G. Diju Daniel UG Scholar Assistant Professor Department of Electronics and Communication Engineering Department of Electronics and Communication Engineering Abstract The work is concerned by implementing Steganography for images with the improvement on both image security and image quality. Here the algorithm is LSB (Least Significant Bit). The LSB method is one of the best methods for hiding the message. It helps to transferring of the message from one place to another place in secure manner. But, in today s retrieval of hiding message possible to the third person. All kinds of message are stored into the cover image based on LSB. One of the main problem is that stored messages are may be retrieved by another person, because previous method partially hidden the message. The process is improving the steganography method using QR-code data input pattern image and LSB technique in RGB image. In this paper proposed a LSB method used to hidden the message clearly to the secure transferring. Suppose if we are not satisfied with this message hiding, the another secure method of QR code pattern image was used for message hiding. This paper shows that the proposed the message transferring effectively in secure manner based on Least Significance Bit method and QR code pattern image. Thus the proposed system is to increase the system performance. Keywords: Steganography; LSB; RGB; image security; image quality; QR-code I. INTRODUCTION The term Steganography means covered writing from Greek. In Steganography, it provides secrecy of text or images to prevent them from attackers. Steganography provides secret communication which intended hacker unable to find the presence of data or message in it. The concept is that it has a cover object that is used to hide the original message image, a host that is the main image which is to be transmitted. A stego key which is used to hide the message image into cover image, and the steganography algorithm to carry out the required object. The output which we get is stego image contains the hidden message. Then the stego image is sent to the receiver which retrieves the message image by applying the de-steganography. So, the LSB based stegano graphic data embedding have the advantages are simple to understand, easy to implement, and it produces stego-image that look alike a normal image and not visible in naked eyes. QR Code Fig1 & 2: Sender and Receiver of Steganography. QR is short for quick response (they can be read quickly by a cell phone). They are used to take a piece of information from a transitory media and put it in your cell phone. You may soon see QR codes in a magazine advert, on a billboard, a web page or even on someone s T-shirt. The reason why they are more powerful than a standard bar code is that they can store much more data, including URL links, geo coordinates, and text. The other features of QR code is instead of requiring a chunky hand-held scanner to scan them, many modern cell phone scan them. All rights reserved by 327

2 LSB Data Hiding Using LSB with QR Code Data Pattern Image The Least Significant Bit (LSB) is one of the main techniques in spatial domain image steganography. LSB is the lowest significant bit in the byte value of an image pixel. The LSB based image steganography embeds the secret in least significant bits of pixels values of the cover image. It exploits the fact that the level of precision in many image formats is far greater than that perceivable by average human vision. Therefore, an altered image with slight variations in colours will be indistinguishable from the original by ahuman being, just by looking at it. In LSB technique just four byte of pixels are sufficient to hold one message byte. Rest of bits in the pixel remains the same. RGB The RGB colour model is an additive colour model in which Red, Green, and Blue light are added together in various ways to reproduce a broad array of colours. The main purpose of RGB colours model is for the sensing, representation and display of images in electronic system, such as televisions and computers, though it has also been used in conventional photography. RGB is a device-dependent colour model, different devices detect or reproduce a given RGB value differently, since the colour elements and their response to the individual R, G, and B levels vary from the manufacturer to manufacturer, or even in the same device over time. Thus an RGB value does not define the same colours across devices without some kind of colour management. II. EXISTING SYSTEM Today the generation of communication technologies are high, by capable of decoding and accessing on line resources as well as its high storage capacity and decoding speed. So, the progress of making the transfer of data or message in secret way is more important. The binary image steganography aims to reduce the embedding distortion on the texture. But the security of the data is less. Given binary images are converted to vector pixels then Selecting the random pixel values secure image was stored into the pixel values. Then the process is hiding the secured data in an image. But using this technique takes more power and increase the circuit complexity. Some other techniques used in existing ways are Scrambling and Bit flipping. Scrambling technology is an image encryption technology that develops with importance of secure images, but also can be made in frequency domain. Legal users not only can freely control the selection of algorithms and parameters also can use random number technology. Another method is Bit flipping algorithm. It starts out with sequence Y=(Y 1,Y 2,.Y m). Each check node introduces a constraint. As we saw in the previous lecture in each step, the algorithm tries to find a bit flipping which reduces the number of unsatisfied clauses. The algorithm stops when for every bit. The number of unsatisfied constraints is less or equal to the number of satisfied constraints. Also the performance level of a system gets affected and consumes more time period. It also has a chance to get hacked the system. III. PROPOSED SYSTEM This project work proposes a data hiding in a generated QR code image which is hided in a cover image using LSB technique. So, the process is the identification of the secret message hided in the input cover image. The secret message will be transferred from sender to receiver where they access it. The secret message could be in the form of text data. Here, the work is to embed QR code in color image by using data transmission and reception process. Hiding of information techniques would be continually introduced. Also the degrees of complexity are increased. Thus the future malware related traffic could be harder to detect. Fig. 3: Secret message embedded QR code. All rights reserved by 328

3 By using LSB technique, the data that is to be hided is embedded in the images. The neighboring pixel points in the images were identified based on the embedding data and they are replaced by the data. The message bits are embedded into the right-most LSBs of an m-bit pixel, other bits are adjusted by a simple evaluation. This will be execution process and the de-steganography method retrieves the message to receiver. Encryption of data and hiding data from unauthentic usage is very important. This problem can be solved by encrypting the data and it in a QR code, which can be kept with the person all the time and the QR code scanner with software can be used to decode with the authentic password the information saved in it. In this system the RGB cover image, choosing any of that RGB band that is R band or G band or B band, for than LSB value of binary values are replace by the QR code pattern image LSB binary values. By that way the message is hidden into the cover image. Block Diagram Pre-Processing Work Fig. 4: Block Diagram First we apply the input data pattern, then to convert the string data format to binary data format. Then get the binary data and to apply the QR code creation process. This work is to modify the data arrangement in square matrix format. This work is to check the position matrix and alignment matrix for required QR code pattern image creation. Then select the any one RGB image and to split band wise manner and to get the all pixel value to any one text file. This is our overall pre-processing work. QR-Code Image Creation In this method the QR code select the modified pixels accurately and quickly compare to the existing algorithm. QR codes have already overtaken the popularity of classical barcode in many areas because of several advantages like increase in capacity. QR codes are capable of symbolizing same amount of data in approximately one tenth the space of a traditional barcode. The finder outlines located at three turns of the symbol projected to assist in easy setting of its position, size and penchant. Image Embedded LSB Technique We apply the theory of hypothesis testing to the steg analysis, or detection of hidden data, in the least significant bit (LSB) of a host image. We provide a probabilistic description of the host and the LSB hiding mechanism, which is central to the study of statistical steg analysis tools. We apply the LSB technique, this technique is to hiding the encrypted information in 3-band level. So do not change the cover image level. Inverse Qr-Code Data Pattern Image Creation In this process we can get the inverse QR-code data pattern image in the receiver side This inverse QR-code data pattern image is same for sender side of the QR-code data pattern image. This QR-code image includes the input text data. Final Information Retrival Process In this process we can get the QR-code data pattern image and original input data from cover image. Finally, we can get the original input text data in receiver side. This output text data is same for input text data. All rights reserved by 329

4 IV. SOFTWARE SPECIFICATIONS As of software requirements, here we are using Windows 7 operating system for the process. MATLAB 2013A and XILINX 14.2will be the software for developing the coding of creating secret message and hiding in another image. The language will be the VHDL module. V. SIMULATION Fig. 5: Giving an Input Message to generate QR code data pattern image. Initially, the input message must be given for generating input QR code data pattern. Fig. 6: Generated QR code data pattern image. The input message which we given for hiding in an image is generated in an QR code. All rights reserved by 330

5 Fig. 7: Selecting of cover image. After generating the QR code, we should select a cover image for hiding the QR code. We can give any messages as we like. So, if any retrieve malware happens, they cannot scan the QR code because it is a generated lookalike QR data pattern image. Fig. 8: Selected cover image. The above figure is the selected cover image to hide the QR code. Selecting a cover image is to hide the data binder QR code generated image. So, the process is selecting the cover image will be either from documents or some user decided images. They are used to take a piece of information from a transitory media and put it in your cell phone. The reason why they are more powerful than a standard bar code is that they can store much more data, including URL links, geo coordinates, and text. The LSB based image steganography embeds the secret in least significant bits of pixels values of the cover image. It exploits the fact that the level of precision in many image formats is far greater than that perceivable by average human vision. This method gives efficient result. Fig. 9: Simulation of embedding the QR code data pattern image in a selected cover image. All rights reserved by 331

6 After the selecting process done, the simulation has processed for hiding the QR data pattern image in a cover image. Even if any illegal retrieval steps are taken by the hackers, they cannot hack the original message. Fig. 10: Resulted QR code data pattern hided in a cover image. Thus, the final result will be the generated QR code data pattern image is successfully hided in a selected cover image. VI. CONCLUSION Thus, we design a data hiding in RGB image. This work is to improve the steganography methodology using QR- data pattern image and LSB technique. Skillful techniques of code pattern design, unit block segmentation, pattern block classification, and so on, have been proposed for message data embedding and extraction. So, the proposed system process has done the message transferring effectively in secure manner based on Least Significance Bit method and QR code pattern image. Finally, the resulted work is to increase the system performance level compare to existing methodology. VII. FUTURE WORK Due to the rich availability of choices and the masking features offered by the massive utilization in Internet, malware developers could find the highest potential in network steganography. So, the future work will be possible to make deeper analysis in order to understand the steganography process of hider man and masker. The research can be expanded by doing analysis of steganography process of other tools in the audio and video media file. Identifying the maximum capacity of information that can be hidden in an image using a particular steganographic tool has to be modeled. REFERENCES [1] Cheddad, J. Condell, K. Curran, & P. Kevitt, (2010). Digital image Steganography- survey and analysis of current methods. Signal Processing, 90, [2] C. K. Chan, L. M. Cheng, Hiding data in image by simple LSB substitution, pattern recognition, Vol. 37, No. 3, 2004, pp [3] R Z. Wang, C. F. Lin and I. C. Lin, Image Hiding by LSB substitution and genetic algorithm, Pattern Recognition, Vol. 34, No. 3, pp , [4] D. Sandipan, A. Ajith, S. Sugata, An LSB Data Hiding Technique Using Prime Numbers, The Third International Symposium on Information Assurance and Security, Manchester, UK, IEEE CS press, 200 [5] C. Kessler. (2001). Steganography: Hiding Data within Data. An edited version of this paper with the title "Hiding Data in Data". Windows &.NET Magazine. [6] Bingwen Feng, Wei Lu, and Wei Sun, Secure Binary Image Steganography Based on Minimizing the Distortion on the Texture, IEEE Transactions on Information Forensics and Security, vol. 10, no. 2, FEB 2015 [7] D. Yardon, Symantec Develops New Attck on Cyberhacking, Wall Street J., May 2014; /articles/sb [8] E. Zielińska, W. Mazurczyk, and K. Szczypiorski, Trends in Steganography, Comm. ACM, vol. 57, no. 3, 2014, pp [9] O. Dabeer et al., Detection of Hiding in the Least Signifiant Bit, IEEE Trans. Signal Processing, vol. 52, no. 10, 2004, pp [10] W. Mazurczyk and L. Caviglione, Steganography in Modern Smartphones and Mitigation Techniques, IEEE Comm. Surveys & Tutorials, 2014, doi: COMST [11] É. Young and E. Ward, Trojan. Downbot, Symantec Security Response, 24 May 2011; /writeup.jsp?docid= [12] E. Nakashima, Report on Operation Shady RA Identifis Widespread Cyber-Spying, Washington Post, 3 Aug. 2011; post.com/national/national-security /report-identifis-widespread-cyber-spying/2011/07/29/giqaotu mqi_story.html. All rights reserved by 332